We address various fundamental problems in the use of prediction markets for corporate forecasting.
Prediction Markets
Prediction markets are speculative markets created for the purpose of making predictions. Assets are created whose final value is tied to a particular event (e.g., will the next US president be a Republican) or metric (e.g., total sales next quarter). The current market prices (in the prediction market) can then be interpreted as predictions of the probability of the event or the expected value of the metric. Other names for prediction markets include information markets, decision markets, idea futures, event derivatives, and virtual markets.
Prediction markets can be traded using a continuous double auction (matching of buy and sell orders) or an automated market maker.
Corporate Forecasting
Many corporations and organizations forecast their future financial and operational results. These forecasts are typically structured using bottom-up assumptions and inputs. For example, when forecasting sales revenue for the next fiscal year, the forecast could be constructed using revenue per product per region per month. A typical multinational organization may forecast sales on 100 products in 50 countries over 12 months, this would equate to approximately 60,000 inputs (100×50×12). Forecasts are then consolidated to more summary levels and retain consistency through simple math (e.g. Worldwide annual sales of product A=sum of sales for product A across all months across all countries).
Depending on the requirements and the analysis, this forecast can be reported at multiple levels—product A, in Region B in Month 4, or total sales for the year in Region B, or total sales of product D over the first half of the year for example. For these types of analysis, the data should be consistent, for example, the sum of all regions equals the world wide total. In this way, summary data can be “drilled down” to show bottom level data. For example, FIG. 1 shows how worldwide summary data can be drilled down to show regional summary data, and regional summary data can be drilled down to show bottom level product data for that region.